Generative AI Advance Fine-Tuning for LLMs

"Fine-tuning large language models (LLMs) is essential for aligning them with specific business needs, improving accuracy, and optimizing performance. In today’s AI-driven world, organizations rely on fine-tuned models to generate precise, actionable insights that drive innovation and efficiency. This course equips aspiring generative AI engineers with the in-demand skills employers are actively seeking. You’ll explore advanced fine-tuning techniques for causal LLMs, including instruction tunin

• In-demand generative AI engineering skills in fine-tuning LLMs that employers are actively seeking
• Instruction tuning and reward modeling using Hugging Face, plus understanding LLMs as policies and applying RLHF techniques
• Direct preference optimization (DPO) with partition function and Hugging Face, including how to define optimal solutions to DPO problems
• Using proximal policy optimization (PPO) with Hugging Face to build scoring functions and tokenize datasets for fine-tuning